Free-form型机床的切齿优化（一）——优化模型的建立 by 张艳红, 郭九生, 王小椿

“…In this paper, Free-form style machine tool setting parameters is analyzed, and a mathematical model of optimal synthesis is established. Keeping the predetermined second-order contact parameters unchanged and optimizing third-order contact, this model can meanwhjle optimize the forth-order contact parameters, improving quality of the meshing. …”
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A Novel Support Vector Machine with Globality-Locality Preserving by Cheng-Long Ma, Yu-Bo Yuan

“…However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. …”
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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

“…Four machine learning algorithms—Random Forests, Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—were applied alongside a traditional logistic regression model. …”
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The application of blockchain with a comparative approach and its impact on risk, flexibility and internal control evaluation by mohammadsadegh aminipoor, HamidReza GholamniaRoshan, azadeh kiapour, Iman Dadashi

“…Overall, blockchain can help improve risk, flexibility and internal control assessment. …”
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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels by Wan Rahiman, Mohamad Hazwan Mohd Ghazali, Iskandar Al-Thani Mahmood

“…Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. …”
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Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. …”
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Machine learning-based prediction of LDL cholesterol: performance evaluation and validation by Jing-Bi Meng, Zai-Jian An, Chun-Shan Jiang

“…Objective This study aimed to validate and optimize a machine learning algorithm for accurately predicting low-density lipoprotein cholesterol (LDL-C) levels, addressing limitations of traditional formulas, particularly in hypertriglyceridemia. …”
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Residual Film–Cotton Stubble–Nail Tooth Interaction Study Based on SPH-FEM Coupling in Residual Film Recycling by Xuejun Zhang, Yangyang Shi, Jinshan Yan, Shuo Yang, Zhaoquan Hou, Huazhi Li

“…The effect of cotton straw–root stubble on residual film recovery can only be better determined by appropriate machine operating parameters, which are essential to improving residual film recovery. Through analyses of the pickup device, key parameters were identified, and a model was built by combining the FEM and SPH algorithms to simulate the interaction of nail teeth, residual film, soil and root stubble. …”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…This paper proposed an improved PINN, named Uniform Physics Informed Neural Network (UPINN), with Proximal Policy Optimization (PPO) based reinforcement learning, for extortion of parameters of these models. …”
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Software and methodological support for additional professional courses: ideas, problems, monitoring by O. D. Prokhorenko, S. V. Morin, O. A. Kozyreva

“…By building prospects for theorizing and implementing ideas for improving the quality of modeling and using software and methodological support for further education courses, we can distinguish in the integrative representation of the direction and prospects of an individual’s achievements three types of solving professional and pedagogical problems theorized in the work (algorithmic, creative-stimulative and innovative-promising).…”
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Fuzzy incidence coloring under structural operations for communication channel allocation by Angmo Deji, Qian Wang, Li Zhou

“…By applying FICN, the minimum number of frequency signals required to avoid communication interference can be determined, thereby enabling optimized channel allocation, reduced interference, and improved communication management under constrained resources.…”
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Elastic regularization networks for enhanced UAV visual tracking by Qingjiao Meng, Ji Li, Yan Jin, Zhaotian Deng

“…On the DTB70 dataset, the proposed method achieves a precision of 0.747 and a success rate of 0.789, representing improvements of 1% and 2.9%, respectively, over the STRCF algorithm. …”
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Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme by Ying Nie, He Bo, Weiqun Zhang, Haipeng Zhang

“…Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. …”
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The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia by Zhixiang Chen, Zhixiang Chen, Zhimei Chen, Xiaobo Huang, Xiaobo Huang, Xiaobo Huang, Xiongbin Yan, Xiongbin Yan, Xiaolin Lai, Xiaolin Lai, Shaoyuan Wang, Shaoyuan Wang

“…This study aims to establish an IME-related and m7G-related prognostic model for improved risk stratification and personalized treatment in AML.MethodsImmune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. …”
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Ensemble Transformer–Based Detection of Fake and AI–Generated News by Md. Ishraquzzaman, Mohammed Ashraful Islam Chowdhury, Shahreen Rahman, Riasat Khan

“…The proposed ensemble model is optimized by applying model pruning (reducing parameters from 265M to 210M, improving training time by 25%) and dynamic quantization (reducing model size by 50%, maintaining 95.68% accuracy), enhancing scalability and efficiency while minimizing computational overhead. …”
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Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s by Xiuhong Wang, Qingjie Wang, Yichen Qiao, Xinyue Zhang, Caiyun Lu, Chao Wang

“…Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. …”
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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. …”
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…External validation using the REFRAIN cohort confirmed the robustness of model(AUC = 0.776). Conclusions The interpretable predictive model improves mortality risk stratification in ICU patients with hemorrhage, supporting clinicians in optimizing treatment plans and resource allocation. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. …”
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